Machine learning based examination of a semiconductor specimen and training thereof

ABSTRACT

There is provided a system and method of runtime examination of a semiconductor specimen. The method includes obtaining a runtime image representative of an inspection area of the specimen, the runtime image having a relatively low signal-to-noise ratio (SNR); and processing the runtime image using a machine learning (ML) model to obtain examination data specific for a given examination application, wherein the ML model is previously trained for the given examination application using one or more training samples, each training sample representative of a respective reference area sharing the same design pattern as the inspection area and comprising: a first training image of the respective reference area having a relatively low SNR; and label data indicative of ground truth in the respective reference area pertaining to the given examination application, the label data obtained by annotating a second training image of the respective reference area having a relatively high SNR.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a semiconductor specimen, and more specifically, toexamination of a specimen using a specifically trained machine learningmodel.

BACKGROUND

Current demands for high density and performance, associated with ultralarge-scale integration of fabricated devices, require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. As semiconductor processes progress, pattern dimensions,such as line width, and other types of critical dimensions, arecontinuously shrunken. Such demands require formation of device featureswith high precision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including automated examinationof the devices while they are still in the form of semiconductor wafers.

Run-time examination can generally employ a two-phase procedure, e.g.,inspection of a specimen followed by review of sampled locations ofpotential defects. Examination generally involves generating certainoutput (e.g., images, signals, etc.) for a specimen by directing lightor electrons to the wafer and detecting the light or electrons from thewafer. During the first phase, the surface of a specimen is inspected athigh-speed and relatively low-resolution. Defect detection is typicallyperformed by applying a defect detection algorithm to the inspectionoutput. A defect map is produced to show suspected locations on thespecimen having high probability of being a defect. During the secondphase, at least some of the suspected locations are more thoroughlyanalyzed with relatively high resolution, for determining differentparameters of the defects, such as classes, thickness, roughness, size,and so on.

Examination can be provided by using non-destructive examination toolsduring or after manufacture of the specimen to be examined. A variety ofnon-destructive examination tools includes, by way of non-limitingexample, scanning electron microscopes, atomic force microscopes,optical inspection tools, etc. In some cases both the two phases can beimplemented by the same inspection tool, and, in some other cases, thesetwo phases are implemented by different inspection tools.

Examination processes can include a plurality of examination steps. Themanufacturing process of a semiconductor device can include variousprocedures, such as etching, depositing, planarization, growth such asepitaxial growth, implantation, etc. The examination steps can beperformed a multiplicity of times, for example after certain processprocedures, and/or after the manufacturing of certain layers, or thelike. Additionally or alternatively, each examination step can berepeated multiple times, for example for different wafer locations, orfor the same wafer locations with different examination settings.

By way of example, examination processes are used at various stepsduring semiconductor fabrication to detect and classify defects onspecimens, as well as perform metrology related operations.Effectiveness of examination can be improved by automatization ofprocess(es) such as, for example, defect detection, Automatic DefectClassification (ADC), Automatic Defect Review (ADR), image segmentation,automated metrology-related operations, etc. Automated examinationsystems ensure that the parts manufactured meet the quality standardsexpected, and provide useful information on adjustments that may beneeded to the manufacturing tools, equipment and/or compositions,depending on the type of defects identified.

In some cases, machine learning technologies can be used to assist theautomated examination process so as to promote higher yield. Forinstance, supervised machine learning can be used to enable accurate andefficient solutions for automating specific examination applicationsbased on sufficiently annotated training images.

SUMMARY

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a computerized system of runtime examinationof a semiconductor specimen, the system comprising a processing andmemory circuitry (PMC) configured to: obtain a runtime imagerepresentative of an inspection area of the semiconductor specimen, theruntime image having a relatively low signal-to-noise ratio (SNR); andprocess the runtime image using a machine learning (ML) model to obtainexamination data specific for a given examination application, whereinthe ML model is previously trained for the given examination applicationusing one or more training samples, each training sample representativeof a respective reference area sharing same design pattern as theinspection area and comprising: a first training image of the respectivereference area having a relatively low SNR similar to the low SNR of theruntime image; and label data indicative of ground truth in therespective reference area pertaining to the given examinationapplication, the label data obtained by annotating a second trainingimage of the respective reference area having a relatively high SNR.

In addition to the above features, the system according to this aspectof the presently disclosed subject matter can comprise one or more offeatures (i) to (xii) listed below, in any desired combination orpermutation which is technically possible:

-   -   (i). The runtime image and the one or more training samples are        acquired by an electron beam tool.    -   (ii). The given examination application is one of: a        segmentation application for segmenting the runtime image into        one or more segments in the inspection area, a metrology        application for obtaining one or more measurements with respect        to a structural element in the inspection area, a defect        detection application for detecting one or more defect        candidates in the inspection area, and a defect classification        application for classifying one or more defects in the        inspection area.    -   (iii). The first training image is generated based on a first        number of training frames acquired for the reference area, and        the second training image is generated based on a second number        of training frames acquired for the reference area. The first        number is smaller than the second number, and the runtime image        is generated based on the first number of runtime frames.    -   (iv). The second number of training frames comprises the first        number of training frames.    -   (v). The first training image is generated based on a first dose        of electrons, and the second training image is generated based        on a second dose of electrons. The first dose is less than the        second dose, and the runtime image is generated based on the        first dose of electrons.    -   (vi). The label data is obtained based on at least one of:        manual annotation, synthetically generated labels based on        design data, machine learning derived labels, or a combination        thereof    -   (vii). The first training image and the second training image        are registered to correct an offset therebetween so that the        first training image and the label data are aligned.    -   (viii). The first training image is acquired prior to        acquisition of the second training image.    -   (ix). The inspection area is a part of a sensitive layer of the        semiconductor specimen which is inspectable only via images        having a relatively low SNR.    -   (x). The given examination application is a segmentation        application for segmenting the runtime image into one or more        segments in the inspection area, and the label data is        indicative of a specific segment of the one or more segments        that each pixel of at least part of the runtime image belongs        to.    -   (xi). The inspection area is from an inspection die of the        semiconductor specimen, and the respective reference area is        from a reference die of the semiconductor specimen or of a        different semiconductor specimen.    -   (xii). The relatively low SNR and the relatively high SNR are        relative to each other, or relative to a threshold.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method of runtime examination of asemiconductor specimen, the method performed by a processor and memorycircuitry (PMC) and comprising: obtaining a runtime image representativeof an inspection area of the semiconductor specimen, the runtime imagehaving a relatively low signal-to-noise ratio (SNR); and processing theruntime image using a machine learning (ML) model to obtain examinationdata specific for a given examination application, wherein the ML modelis previously trained for the given examination application using one ormore training samples, each training sample representative of arespective reference area sharing the same design pattern as theinspection area and comprising: a first training image of the respectivereference area having a relatively low SNR similar to the low SNR of theruntime image; and label data indicative of ground truth in therespective reference area pertaining to the given examinationapplication, the label data obtained by annotating a second trainingimage of the respective reference area having a relatively high SNR.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method of training a machine learning modelusable for examining a semiconductor specimen, the method performed by aprocessor and memory circuitry (PMC) and comprising: obtaining atraining set comprising one or more training samples, each trainingsample representative of a respective reference area having a givendesign pattern, the training sample comprising: a first training imagehaving a relatively low signal-to-noise ratio (SNR); and label dataindicative of ground truth in the respective reference area pertainingto the given examination application, the label data obtained byannotating a second training image of the respective reference areahaving a relatively high SNR; and training the ML model for the givenexamination application using the training set; wherein the ML model,upon being trained, is usable for processing a runtime imagerepresentative of an inspection area of the specimen sharing the samedesign pattern as the given design pattern and obtaining examinationdata specific for the given examination application, the runtime imagehaving a relatively low SNR similar to the low SNR of the first trainingimage.

These aspects of the disclosed subject matter can comprise one or moreof features (i) to (xii) listed above with respect to the system,mutatis mutandis, in any desired combination or permutation which istechnically possible.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform a method for runtime examination of a semiconductorspecimen, the method comprising: obtaining a runtime imagerepresentative of an inspection area of the semiconductor specimen, theruntime image having a relatively low signal-to-noise ratio (SNR); andprocessing the runtime image using a machine learning (ML) model toobtain examination data specific for a given examination application,wherein the ML model is previously trained for the given examinationapplication using one or more training samples, each training samplerepresentative of a respective reference area sharing same designpattern as the inspection area and comprising: a first training image ofthe respective reference area having a relatively low SNR similar to thelow SNR of the runtime image; and label data indicative of ground truthin the respective reference area pertaining to the given examinationapplication, the label data obtained by annotating a second trainingimage of the respective reference area having a relatively high SNR.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform a method for training a machine learning modelusable for examining a semiconductor specimen, the method comprising:obtaining a training set comprising one or more training samples, eachtraining sample representative of a respective reference area having agiven design pattern, the training sample comprising: a first trainingimage having a relatively low signal-to-noise ratio (SNR); and labeldata indicative of ground truth in the respective reference areapertaining to the given examination application, the label data obtainedby annotating a second training image of the respective reference areahaving a relatively high SNR; and training the ML model for the givenexamination application using the training set; wherein the ML model,upon being trained, is usable for processing a runtime imagerepresentative of an inspection area of the specimen sharing the samedesign pattern as the given design pattern and obtaining examinationdata specific for the given examination application, the runtime imagehaving a relatively low SNR similar to the low SNR of the first trainingimage.

These aspects of the disclosed subject matter can comprise one or moreof features (i) to (xii) listed above with respect to the system,mutatis mutandis, in any desired combination or permutation which istechnically possible.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a generalized block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 2 illustrates a generalized flowchart of training a machinelearning model usable for examining a semiconductor specimen inaccordance with certain embodiments of the presently disclosed subjectmatter.

FIG. 3 illustrates a generalized flowchart of runtime examination of asemiconductor specimen using a trained ML in accordance with certainembodiments of the presently disclosed subject matter.

FIG. 4 illustrates a generalized flowchart of generating a training setfor training a ML model usable for examining a semiconductor specimen inaccordance with certain embodiments of the presently disclosed subjectmatter.

FIG. 5 shows a schematic illustration of a training process of a MLmodel in accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 6 illustrates an example of a first training image in accordancewith certain embodiments of the presently disclosed subject matter.

FIG. 7 is a schematic illustration of a runtime examination processusing a ML model in accordance with certain embodiments of the presentlydisclosed subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “obtaining”, “processing”,“training”, “acquiring”, “segmenting”, “detecting”, “classifying”,“generating”, “registering”, or the like, refer to the action(s) and/orprocess(es) of a computer that manipulate and/or transform data intoother data, said data represented as physical, such as electronic,quantities and/or said data representing the physical objects. The term“computer” should be expansively construed to cover any kind ofhardware-based electronic device with data processing capabilitiesincluding, by way of non-limiting example, the examination system, thetraining system, and respective parts thereof disclosed in the presentapplication.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter. The terms should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterms shall also be taken to include any medium that is capable ofstoring or encoding a set of instructions for execution by the computerand that cause the computer to perform any one or more of themethodologies of the present disclosure. The terms shall accordingly betaken to include, but not be limited to, a read only memory (“ROM”),random access memory (“RAM”), magnetic disk storage media, opticalstorage media, flash memory devices, etc.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of physical objects or substrates includingwafers, masks, reticles, and other structures, combinations and/or partsthereof used for manufacturing semiconductor integrated circuits,magnetic heads, flat panel displays, and other semiconductor-fabricatedarticles. A specimen is also referred to herein as a semiconductorspecimen, and can be produced by manufacturing equipment executingcorresponding manufacturing processes.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of operations related to defect detection,defect review and/or defect classification of various types,segmentation, and/or metrology operations during and/or after thespecimen fabrication process. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),imaging, sampling, detecting, reviewing, measuring, classifying and/orother operations provided with regard to the specimen or parts thereof,using the same or different inspection tools. Likewise, examination canbe provided prior to manufacture of the specimen to be examined, and caninclude, for example, generating an examination recipe(s) and/or othersetup operations. It is noted that, unless specifically statedotherwise, the term “examination” or its derivatives used in thisspecification are not limited with respect to resolution or size of aninspection area. A variety of non-destructive examination toolsincludes, by way of non-limiting example, scanning electron microscopes(SEM), atomic force microscopes (AFM), optical inspection tools, etc.

The term “metrology operation” used in this specification should beexpansively construed to cover any metrology operation procedure used toextract metrology information relating to one or more structuralelements on a semiconductor specimen. In some embodiments, the metrologyoperations can include measurement operations, such as, e.g., criticaldimension (CD) measurements performed with respect to certain structuralelements on the specimen, including but not limiting to the following:dimensions (e.g., line widths, line spacing, contact diameters, size ofthe element, edge roughness, gray level statistics, etc.), shapes ofelements, distances within or between elements, related angles, overlayinformation associated with elements corresponding to different designlevels, etc. Measurement results such as measured images are analyzed,for example, by employing image-processing techniques. Note that, unlessspecifically stated otherwise, the term “metrology” or derivativesthereof used in this specification are not limited with respect tomeasurement technology, measurement resolution, or size of inspectionarea.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirablefeature/functionality formed on a specimen. In some cases, a defect maybe a defect of interest (DOI) which is a real defect that has certaineffects on the functionality of the fabricated device, thus is in thecustomer's interest to be detected. For instance, any “killer” defectsthat may cause yield loss can be indicated as a DOI. In some othercases, a defect may be a nuisance (also referred to as a “false alarm”defect) which can be disregarded because it has no effect on thefunctionality of the completed device and does not impact yield.

The term “defect candidate” used in this specification should beexpansively construed to cover a suspected defect location on thespecimen which is detected to have relatively high probability of beinga defect of interest (DOI). Therefore, a defect candidate, upon beingreviewed, may actually be a DOI, or, in some other cases, it may be anuisance as described above, or random noise that can be caused bydifferent variations (e.g., process variation, color variation,mechanical and electrical variations, etc.) during inspection.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g., throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format, orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter.

The examination system 100 illustrated in FIG. 1 can be used forexamination of a semiconductor specimen (e.g., a wafer, a die, or partsthereof) as part of the specimen fabrication process. As describedabove, the examination referred to herein can be construed to cover anykind of operations related to defect inspection/detection, defectclassification of various types, segmentation, and/or metrologyoperations, such as, e.g., critical dimension (CD) measurements, withrespect to the specimen. The illustrated examination system 100comprises a computer-based system 101 capable of enabling automaticexamination of a semiconductor specimen based on machine learning (ML).According to certain embodiments of the presently disclosed subjectmatter, system 101 can be configured to examine a semiconductor specimenin runtime using a trained machine learning (ML) model based on imagesobtained during specimen fabrication (referred to herein also asfabrication process (FP) images or runtime images). In some embodiments,system 101 can be configured as a training system capable of training aML model during a training/setup phase using a specifically generatedtraining set.

System 101 can be operatively connected to one or more examination tools120. The examination tools 120 are configured to capture runtime imagesand/or training images, process the captured images, and/or providemeasurements related to the captured images.

By way of example, the runtime images and/or training images used hereincan refer to original images of a specimen captured during themanufacturing process, derivatives of the captured images obtained byvarious pre-processing stages, and/or computer-generated designdata-based images. For instance, the images can be selected from, e.g.,images of a part of a specimen captured by a scanning electronmicroscope (SEM) or an optical inspection system, SEM images roughlycentered around the defect to be classified by ADC, SEM images of largerregions in which the defect is to be localized by ADR, registered imagesof different examination modalities corresponding to the same location,segmented images, or height map images, etc. It is to be noted that insome cases the images referred to herein can include image data (e.g.,captured images, processed images, etc.) and associated numeric data(e.g., metadata, hand-crafted attributes, etc.). It is further notedthat image data can include data related to a layer of interest and/orto one or more layers of the specimen.

The term “examination tool(s)” used herein should be expansivelyconstrued to cover any tools that can be used in examination-relatedprocesses, including, by way of non-limiting example, imaging, scanning(in a single or in multiple scans), sampling, reviewing, measuring,classifying and/or other processes provided with regard to the specimenor parts thereof. The one or more examination tools 120 can include oneor more inspection tools and/or one or more review tools. In some cases,at least one of the examination tools 120 can be an inspection toolconfigured to scan a specimen (e.g., an entire wafer, an entire die, orportions thereof) to capture inspection images (typically, at arelatively high-speed and/or low-resolution) for detection of potentialdefects (i.e., defect candidates). In some cases, at least one of theexamination tools 120 can be a review tool, which is configured tocapture review images of at least some of the defect candidates detectedby inspection tools for ascertaining whether a defect candidate isindeed a defect of interest (DOI). Such a review tool is usuallyconfigured to inspect fragments of a specimen, one at a time (typically,at a relatively low-speed and/or high-resolution). The inspection tooland review tool can be different tools located at the same or atdifferent locations, or a single tool operated in two different modes.In some cases, at least one examination tool can have metrologycapabilities and can be configured to perform metrology operations onthe images.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools 120 can be implemented as inspectionmachines of various types, such as optical inspection machines, electronbeam inspection machines (e.g., Scanning Electron Microscope (SEM),Atomic Force Microscopy (AFM), or Transmission Electron Microscope(TEM), etc.), and so on. In some cases, the same examination tool canprovide low-resolution image data and high-resolution image data. Theresulting image data (low-resolution image data and/or high-resolutionimage data) can be transmitted—directly or via one or more intermediatesystems—to system 101. The present disclosure is not limited to anyspecific type of examination tools and/or the resolution of image dataresulting from the examination tools.

As aforementioned, the examination system 100 comprises a computer-basedsystem 101 capable of examining a semiconductor specimen in runtimeusing a trained machine learning (ML) model based on runtime imagesobtained during specimen fabrication. For training the ML model,sufficient training data is required. However, training images of thespecimens to be inspected may be unavailable during the setup phase. Insome cases, training images of test/reference wafers are used which maynot include sufficient variations of structural parameters to produce awell-trained, accurate model that is robust with respect to processvariations in actual production. Therefore, in order to have the MLtrained to enable accurate examination, oftentimes the training of theML model is performed at the customer site using images acquired fromthe actual production wafers. For instance, SEM images of a productionwafer can be captured in FAB and used for the purpose of training the MLmodel.

A SEM image can be generated by aggregating a sequence of framescaptured for an area of the semiconductor specimen, which aresequentially acquired by an electron beam examination tool such as ascanning electron microscope (SEM). In order to obtain a SEM image withhigher quality (e.g., higher signal-to-noise ratio (SNR)), the electronbeam dose used to scan the wafer (which can be reflected as the numberof frames captured for the area and/or the strength of the electron beamused for capturing the frames) has to reach a certain amount so as toreduce noises in the resultant image.

On the other hand, as a consequence of an impact of the electron beamimpinging on the specimen, the specimen can be physically damaged. Thisphenomenon is referred to as “shrinkage” or “slimming”. For instance, atypical amplitude of shrinkage can be, e.g., between 5% to 10% of thedimensions of the structural features on the wafer, depending on thedifferent layers and/or materials of a specimen, which is not desiredfor the customer. In some cases, in order to reduce such damage on aproduction wafer, in particular on certain sensitive layers of thewafer, the electron dose of the electron beam tool used for acquiringthe frames is restricted. However, the SEM image obtained with suchrestriction is typically very noise with a low SNR. It is undesired, andin some cases even impossible, to perform annotation on a low SNR image,which is very challenging, even for manual annotation by a user. Theresultant annotation on such images is likely to be inaccurate and errorprone. Since the annotated label data are used as ground truth in thetraining of a ML model, the ML model trained using such training datacannot provide accurate and effective examination of the specimens.

Accordingly, certain embodiments of the presently disclosed subjectmatter propose a system (e.g., system 101) capable of training a MLmodel using training samples with specific composition so as to addressthe above issues. Certain embodiments of the present disclosure use a MLmodel trained in such way for runtime examination, as detailed below.

System 101 includes a processor and memory circuitry (PMC) 102operatively connected to a hardware-based I/O interface 126. PMC 102 isconfigured to provide processing necessary for operating the system asfurther detailed with reference to FIGS. 2-4 and comprises a processor(not shown separately) and a memory (not shown separately). Theprocessor of PMC 102 can be configured to execute several functionalmodules in accordance with computer-readable instructions implemented ona non-transitory computer-readable memory comprised in the PMC. Suchfunctional modules are referred to hereinafter as comprised in the PMC.

As aforementioned, in certain embodiments, system 101 can be configuredas a training system capable of training a ML model using trainingsamples during a training/setup phase. In such cases, functional modulescomprised in PMC 102 can include a training set generator 104, atraining module 106, and a machine learning model 108. The training setgenerator 104 can be configured to obtain a training set comprising oneor more training samples. Each training sample is representative of arespective reference area that has a given design pattern. The trainingsample comprises a first training image having a relatively lowsignal-to-noise ratio (SNR), and label data indicative of ground truthin the respective reference area pertaining to the given examinationapplication. The label data is obtained by annotating a second trainingimage of the respective reference area having a relatively high SNR.

The training module 106 can be configured to train a machine learningmodel 108 for the given examination application using the training set.The ML model, upon being trained, is usable for processing a runtimeimage representative of an inspection area sharing the same designpattern as the given design pattern, and obtaining examination dataspecific for the given examination application. The runtime image isacquired with a relatively low SNR. Details of the training process aredescribed below with reference to FIGS. 2 and 4 .

According to certain embodiments, system 101 can be configured toexamine a semiconductor specimen in runtime using the trained ML modelbased on runtime images obtained during specimen fabrication. In suchcases, one or more functional modules comprised in PMC 102 can includethe ML model 108 which has been trained as described above. The PMC 102can be configured to obtain, via the I/O interface 126, a runtime imagerepresentative of an inspection area of the semiconductor specimen. Theruntime image is acquired at a relatively low signal-to-noise ratio(SNR).

The trained ML model 108 is used to process the runtime image to obtainexamination data specific for a given examination application. Asdescribed above, the ML model is previously trained for the givenexamination application using a training set comprising one or moretraining samples. Each training sample is representative of a respectivereference area sharing same design pattern as the inspection area.Similarly, as described above, each training sample comprises a firsttraining image having a relatively low SNR and label data indicative ofground truth in the respective reference area pertaining to the givenexamination application. The label data is obtained by annotating asecond training image of the respective reference area having arelatively high SNR. Details of the runtime examination process aredescribed below with reference to FIG. 3 .

According to certain embodiments, the ML model can be trained fordifferent examination applications, based on specific training imagesand label data pertaining to respective applications. Variousapplications that can be applicable using the present disclosureinclude, but not limited to, the following: a segmentation applicationfor segmenting the runtime image into one or more segments in theinspection area, a metrology application for obtaining one or moremeasurements with respect to a structural element in the inspectionarea, a defect detection application for detecting one or more defectcandidates in the inspection area, and a defect classificationapplication for classifying one or more defects in the inspection area,etc.

Operation of system 101, PMC 102 and the functional modules therein willbe further detailed with reference to FIGS. 2-4 .

According to certain embodiments, the ML model 108 referred to hereincan be implemented as various types of machine learning models, such as,e.g., decision tree, Support Vector Machine (SVM), Artificial NeuralNetwork (ANN), regression model, Bayesian network, orensembles/combinations thereof etc. The learning algorithm used by theML model can be any of the following: supervised learning, unsupervisedlearning, or semi-supervised learning, etc. The presently disclosedsubject matter is not limited to the specific type of ML model or thespecific type or learning algorithm used by the ML model.

In some embodiments, the ML model can be implemented as a deep neuralnetwork (DNN). DNN can comprise a supervised or unsupervised DNN modelwhich includes layers organized in accordance with respective DNNarchitecture. By way of non-limiting example, the layers of DNN can beorganized in accordance with Convolutional Neural Network (CNN)architecture, Recurrent Neural Network architecture, Recursive NeuralNetworks architecture, Generative Adversarial Network (GAN)architecture, or otherwise. Optionally, at least some of the layers canbe organized into a plurality of DNN sub-networks. Each layer of DNN caninclude multiple basic computational elements (CE) typically referred toin the art as dimensions, neurons, or nodes.

Generally, computational elements of a given layer can be connected withCEs of a preceding layer and/or a subsequent layer. Each connectionbetween a CE of a preceding layer and a CE of a subsequent layer isassociated with a weighting value. A given CE can receive inputs fromCEs of a previous layer via the respective connections, each givenconnection being associated with a weighting value which can be appliedto the input of the given connection. The weighting values can determinethe relative strength of the connections and thus the relative influenceof the respective inputs on the output of the given CE. The given CE canbe configured to compute an activation value (e.g., the weighted sum ofthe inputs) and further derive an output by applying an activationfunction to the computed activation. The activation function can be, forexample, an identity function, a deterministic function (e.g., linear,sigmoid, threshold, or the like), a stochastic function, or othersuitable function. The output from the given CE can be transmitted toCEs of a subsequent layer via the respective connections. Likewise, asabove, each connection at the output of a CE can be associated with aweighting value which can be applied to the output of the CE prior tobeing received as an input of a CE of a subsequent layer. Further to theweighting values, there can be threshold values (including limitingfunctions) associated with the connections and CEs.

The weighting and/or threshold values of a deep neural network can beinitially selected prior to training, and can be further iterativelyadjusted or modified during training to achieve an optimal set ofweighting and/or threshold values in a trained DNN. After eachiteration, a difference can be determined between the actual outputproduced by DNN module and the target output associated with therespective training set of data. The difference can be referred to as anerror value. Training can be determined to be complete when a loss/costfunction indicative of the error value is less than a predeterminedvalue, or when a limited change in performance between iterations isachieved. A set of input data used to adjust the weights/thresholds of adeep neural network is referred to as a training set.

It is noted that the teachings of the presently disclosed subject matterare not bound by specific architecture of the ML or DNN as describedabove.

In some cases, additionally to system 101, the examination system 100can comprise one or more examination modules, such as, e.g., defectdetection module and/or Automatic Defect Review Module (ADR) and/orAutomatic Defect Classification Module (ADC) and/or a metrology-relatedmodule and/or other examination modules which are usable for examinationof a semiconductor specimen. The one or more examination modules can beimplemented as stand-alone computers, or their functionalities (or atleast part thereof) can be integrated with the examination tool 120. Insome cases, the ML model 108 can be comprised in the one or moreexamination modules. Optionally, the ML model 108 can be shared betweenthe examination modules or, alternatively, each of the one or moreexamination modules can comprise its own ML model 108.

According to certain embodiments, system 101 can comprise a storage unit122. The storage unit 122 can be configured to store any data necessaryfor operating system 101, e.g., data related to input and output ofsystem 101, as well as intermediate processing results generated bysystem 101. By way of example, the storage unit 122 can be configured tostore runtime images/training images and/or derivatives thereof producedby the examination tool 120. Accordingly, the images can be retrievedfrom the storage unit 122 and provided to the PMC 102 for furtherprocessing.

In some embodiments, system 101 can optionally comprise a computer-basedGraphical User Interface (GUI) 124 which is configured to enableuser-specified inputs related to system 101. For instance, the user canbe presented with a visual representation of the specimen (for example,by a display forming part of GUI 124), including image data of thespecimen. The user may be provided, through the GUI, with options ofdefining certain operation parameters, such as, e.g., a threshold withrespect to SNR, the number of image frames to be captured, the specificexamination application, etc. For instance, in some cases, the user canprovide label data associated with a second training image by manuallyannotating on the image via the GUI 124. The user may also view theoperation results, such as, e.g., examination data specific for a givenexamination application, on the GUI. In some cases, system 101 can befurther configured to send, via I/O interface 126, the examination datato the examination tool 120 for further processing. In some cases,system 101 can be further configured to send some of the examinationdata to the storage unit 122, and/or external systems (e.g., YieldManagement System (YMS) of a FAB).

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1 ; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and/or hardware.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in other embodiments atleast some of examination tools 120, storage unit 122 and/or GUI 124 canbe external to the examination system 100 and operate in datacommunication with system 101 via I/O interface 126. System 101 can beimplemented as stand-alone computer(s) to be used in conjunction withthe examination tools, and/or with the additional examination modules asdescribed above. Alternatively, the respective functions of the system101 can, at least partly, be integrated with one or more examinationtools 120, thereby facilitating and enhancing the functionalities of theexamination tools 120 in examination-related processes.

While not necessarily so, the process of operation of systems 101 and100 can correspond to some or all of the stages of the methods describedwith respect to FIGS. 2-4 . Likewise, the methods described with respectto FIGS. 2-4 and their possible implementations can be implemented bysystems 101 and 100. It is therefore noted that embodiments discussed inrelation to the methods described with respect to FIGS. 2-4 can also beimplemented, mutatis mutandis as various embodiments of the systems 101and 100, and vice versa.

For purpose of illustration only, certain embodiments of the followingdescription may be provided for training a ML model usable for asegmentation application. Those skilled in the art will readilyappreciate that the teachings of the presently disclosed subject matterare also applicable to various other examination applications, such as,for example, defect detection, ADR, ADC, metrology-related modules, andalike.

Referring to FIG. 2 , there is illustrated a generalized flowchart oftraining a machine learning model usable for examining a semiconductorspecimen in accordance with certain embodiments of the presentlydisclosed subject matter.

Training data used for training a ML model in supervised learningnormally include one or more training samples, each including arespective training image and corresponding ground truth data associatedtherewith. Ground truth data can include label data of the trainingimage which is indicative of application-specific information. By way ofexample, for an examination application of image segmentation, eachtraining sample can include a training image of the semiconductorspecimen and label data indicative of one or more segments in thetraining image.

A training image can be a “real world” image of a semiconductor specimenobtained in a fabrication process thereof. Depending on differentexamination applications, the training image can be obtained in variousways. By way of non-limiting example, the image can be an inspectionimage obtained by examining a specimen using one or more inspectiontools for an application of detection of defect candidates. In anotherexample, the image can be a review image obtained by examining thespecimen at defect candidate locations using one or more review tools,for a defect review application of ascertaining whether a defectcandidate detected by the inspection tools is indeed a defect, and/orfor a defect classification application of ascertaining the class/typeof the defect. Such review tools can be, e.g., a scanning electronmicroscope (SEM), etc.

Ground truth data can be obtained in various ways, such as, e.g., bymanual annotation, synthetic generation based on design data, machinelearning based, or a combination of the above, as will be detailed belowwith reference to FIG. 4 .

As mentioned above, a ML model used for examining a semiconductorspecimen is often trained at the customer site using production waferdata. In order to reduce the damage of the production wafer caused bythe image acquisition (e.g., by SEM), the electron dose of the electronbeam tool used for acquiring the frames (which can be represented by thenumber of frames captured and/or the strength of the electron beam used)should be restricted. However, the SEM images obtained with lesselectron dose are typically very noise with a low SNR. It is verydifficult to make accurate annotation on such images, and in some caseseven impossible. Therefore, the present disclosure proposes to acquirethe training samples in a specific way for addressing the above issues,as detailed below with reference to FIGS. 2 and 4 .

As described in FIG. 2 , a training set can be obtained (202) (e.g., bythe training set generator 104 in PMC 102), comprising one or moretraining samples. Each training sample is representative of a respectivereference area of a specimen that has a given design pattern. In someembodiments, the given design pattern can be a pattern of interest to beexamined on the specimen.

Specifically, the training sample comprises a first training image (204)having a relatively low signal-to-noise ratio (SNR) (e.g., relativelylower with respect to a higher SNR of a second training image, asdescribed below, or with respect to a threshold), and label data (206)indicative of ground truth in the respective reference area pertainingto the given examination application. The label data is obtained byannotating a second training image of the respective reference areahaving a relatively high SNR (e.g., relatively higher with respect tothe threshold). The ML model (e.g., the ML model 108) can be trained(208) (e.g., by the training module 106 in PMC 102) for the givenexamination application using the training set.

In some embodiments, when examining an area on the specimen, a pluralityof frames of the area can be sequentially acquired by the examinationtool (such as an electron beam tool, e.g., SEM). The frames acquired bythe electron beam tool are subsequently aggregated to generate a finalimage, such as, e.g., a SEM image (e.g., by combining/averaging theplurality of frames so as to reduce the noises in the resultant image).As aforementioned, the electron dose of the electron beam tool used foracquiring the frames of a production wafer should be restricted for thepurpose of reducing damage of the production wafer caused by the imageacquisition. By way of example, the dose of electrons used can bereflected as the number of frames captured for the given area and/or thestrength of the electron beam used for capturing the frames. Forinstance, for purpose of damage reduction, during runtime examination ofan area of the specimen, a relatively small number of frames arecaptured for generating the SEM image. By way of another example, anelectron beam with a smaller energy level can be used for generating theSEM image. A SEM image generated as such, however, tends to have a lowerSNR, and is not suitable for image annotation.

Therefore, it is proposed that for one or more reference areas on aspecimen that share the same given design pattern, two images can becaptured for each reference area, including a first training image witha relatively low SNR, and a second training image with a relatively highSNR. In particular, the first training image has the same/similar SNR asthe SNR of a runtime image which will be captured in production time(after the training and deployment of the ML model in production) andwill be examined using the trained ML model. As aforementioned, thelevel of SNR of an image is generally correlated with the dose ofelectrons used for examining the specimen and generating the image.

Specifically, in some embodiments, the first training image can begenerated based on a first dose of electrons, and the second trainingimage can be generated based on a second dose of electrons, where thefirst dose of electrons is less/smaller than the second dose. Inparticular, the first dose of electrons used to capture the firsttraining image is the same as the dose of electrons used for capturing aruntime image which will be examined using the trained ML model. It isto be noted that the term “the same” used herein can refer to eitherbeing identical, or similar to, or highly correlated with each other.Various similarity measures and algorithms can be used for determiningthe level of equivalence/similarity therebetween. Thus, wherever theterm “the same” is used, it should not be limited to be exactly thesame, but rather being similar/equivalent to a certain extent.

By way of example, as the dose of electrons can be reflected as thenumber of frames and/or the strength of the electron beam used forcapturing the image, in some cases the first training image can begenerated based on a first number of training frames acquired for thereference area, and the second training image can be generated based ona second number of training frames acquired for the reference area,where the first number is smaller than the second number. It is to benoted that the number of runtime frames used to generate a runtime imageis the same as the first number (the number of frames used to generatethe first training image), so as to ensure the similarity level of SNRbetween the first training image and the runtime image. In some cases,the first number (the number of frames used to generate the firsttraining image) and the second number (the number of frames used togenerate the second training image) can be defined according to thegiven examination application. By way of example, for a segmentationapplication, the first number (which corresponds to the number ofruntime frames used to generate a runtime image) can be determined basedon a performance requirement of the application, such as, e.g., damagelevel of the specimen that can be accepted, accuracy, throughput, etc.The second number can be determined so as to result in a second trainingimage having sufficient quality to ensure the level of annotationaccuracy, while maintaining the relevancy/correspondence to the firsttraining image in terms of, e.g., dimensions, patterns etc. Forinstance, in some cases, the first number of training frames can be, forinstance, around 10-20 frames, and the second number of training framescan be, for instance, around 50-60 frames.

The one or more reference areas on a specimen that share the same givendesign pattern can be identified in various ways. By way of example,design data of a die (or portion(s) thereof) can comprise various designpatterns which are of specific geometrical structures and arrangements.In some embodiments, the design data can be received, and a plurality ofdesign groups, each corresponding to one or more die areas having thesame design pattern, can be retrieved. Therefore, the areas in the diethat correspond to the same design pattern can be identified. In someembodiments, the inspection area is from an inspection die of thesemiconductor specimen, and a reference area can be from a reference dieof the inspection die, the reference die either from the samesemiconductor specimen, or from a different semiconductor specimen.

It is to be noted that, similarly as defined above, design patterns canbe deemed as “the same”, either when they are identical, or when theyare highly correlated, or similar to each other. Various similaritymeasures and algorithms can be applied for matching and clusteringsimilar design patterns, and the present disclosure should not beconstrued to be limited by any specific measures used for deriving thedesign groups. The clustering of design groups (i.e., the division fromCAD data to the plurality of design groups) can be performed beforehand,or by the PMC 102 as a preliminary step of the presently disclosedprocess.

It is to be noted that the relatively low SNR and relatively high SNR insome cases can be defined with respect to a threshold which may be apredetermined SNR level pertaining to a specific examination application(e.g., a SNR that is sufficient for the application to meet performancerequirements with respect to, e.g., sensitivity, accuracy, throughput,etc.). In some cases, the relatively low SNR and relatively high SNR canbe defined with respect to each other, e.g., as long as the low SNR isrelatively lower than the high SNR, or the high SNR is relatively higherthan the low SNR. As described above with respect to determination ofthe first number of frames, the low SNR (which corresponds to the SNRlevel of the runtime image) can be defined, e.g., based on a performancerequirement of the specific examination application, such as, e.g.,damage level of the specimen that can be accepted, accuracy, throughput,etc. The high SNR can be defined to ensure the level of annotationaccuracy on the second training image, while maintaining therelevancy/correspondence of the second training image to the firsttraining image in terms of, e.g., dimensions, patterns etc.

FIG. 4 illustrates a generalized flowchart of generating a training setfor training a ML model usable for examining a semiconductor specimen inaccordance with certain embodiments of the presently disclosed subjectmatter.

For a given reference area (of the one or more reference areas), a firsttraining image with a low SNR can be acquired (402) (e.g., by theexamination tool 120). A second training image with a high SNR can beacquired (404) (e.g., by the examination tool 120). By way of example,as described above, the first training image can be generated based on afirst number (N₁) of training frames (e.g., 10-20 frames) acquired for areference area, and the second training image can be generated based ona second number (N₂) of training frames (which is larger than the firstnumber, e.g., 50-60 frames) acquired for the reference area.

In some embodiments, the first training image should be acquired beforeacquiring the second training image. This is because the first trainingimage is supposed to represent a similar image condition as the runtimeimage obtained in the production phase. As is known, during the imageacquisition by an electron beam tool, the surface of the specimen isscanned with a focused beam of electrons and the specimen continuouslycollects charges. The buildup of the surface charge on a specimen causedby the electron beam may cause image artifacts, such as, e.g., imagedistortion, variations related to gray level, contrast, edge sharpness,etc. Therefore, in order for the first training image to be acquired ina similar condition as a runtime image, for each given area, the firsttraining image should be acquired first, before accumulation of furthercharging effects on the specimen, similarly as when the runtime image isacquired in runtime. The second image can be acquired after acquisitionof the first image, without changing any tool configurations.

In some embodiments, the first training image is first acquired, e.g.,by acquiring N₁ frames. Then the second training image is acquired,e.g., by acquiring N₂ frames (N₂≥N₁). In some cases, the N₁ frames canbe a part of the N₂ frames. For instance, SEM can first acquire N₁frames which will be combined into the first training image. Then SEMcan continue to acquire (N₂−N₁) frames which will be combined togetherwith the N₁ frames to form the second training images. In some othercases, the N₂ frames can be separately acquired, not including the N₁frames.

In some embodiments, optionally, a training image can comprise multiplechannels captured from different perspectives. For instance, one channelcan be taken from a perpendicular perspective by a top detector of theexamination tool, and another channel can be taken by a side detector ofthe examination tool from a side perspective. In some cases, there canbe more than one side detector from different angles, and, accordingly,the training image can comprise multiple side-channel images. In somecases, the multiple channel images can be combined into one combinedimage.

The second training image with the high SNR is then annotated (406)(e.g., by the training set generator 104). As aforementioned, it ispreferred to perform annotation on a high SNR image so as to improve theaccuracy of the annotated label data which will be used as ground truthdata for training the ML model. The label data can be obtained invarious ways. By way of example, the label data can be obtained bymanual annotation, or can be synthetically produced (e.g., using designdata such as CAD-based images). By way of another example, the groundtruth data can be generated based on machine learning. For instance, amachine learning model can be trained using manually annotated images,and the trained model can be used to automatically (orsemi-automatically) generate label data for input images. An example ofa machine learning based label data generation system is described inU.S. patent application Ser. No. 16/942,677 titled “GENERATING TRAININGDATA USABLE FOR EXAMINATION OF A SEMICONDUCTOR SPECIMEN” which isincorporated herein by reference by its entirety. In some cases, thelabel data can be generated as a combination of any of the above. Thepresent disclosure is not limited to a specific way of obtaining thelabel data of the second training image.

The first training image and the second training image can be registered(408) for purpose of correcting an offset therebetween, so that thefirst training image and the label data are aligned. The offset betweenthe two training images can be caused by various factors, such as, e.g.,drifts caused by charging effects, by the working point of the tool(e.g., scanner and/or stage drift), and/or by shrinkage of the specimen,etc. The image registration as referred to in the present disclosure caninclude measuring an offset between two images, and shifting one imagerelative to the other in order to correct the offset. In particular, inthe present disclosure, once the offset is measured, the label data asannotated in the second training image (i.e., the high SNR image) can beshifted (with or without the second image itself) in accordance with theoffset (relative to the first training image), in order to be alignedwith the low SNR image. Alternatively, the first training image can beshifted in accordance with the offset (relative to the second trainingimage) to be aligned with the label data (as well as with the secondtraining image).

The registration can be implemented according to any suitableregistration algorithms known in the art. By way of example, theregistration can be performed using one or more of the followingalgorithms: an area-based algorithm, feature based registration, orphase correlation registration. An example of an area-based method isregistration using optical flow such as the Lucas-Kanade (LK) algorithm.Feature based methods are based on finding distinct informative points(“features”) in two images, and calculating the needed transformationbetween each pair, based on correspondence of the features. This allowsfor an elastic registration (i.e., non-rigid registration), wheredifferent areas are moved separately. Phase correlation registration(PCR) is done using frequency domain analysis (where phase difference inthe Fourier domain is translated into registration in the image domain).

A training sample for training the ML model is thus generated (410)including the registered first training image and the label data. Insome embodiments, one or more additional training samples can beacquired in a similar manner from one or more additional referenceareas.

A training set comprising one or more training samples generated asdescribed in FIG. 4 can be used to train the ML model in a supervisedmanner. The ML model, upon being trained, is usable for processing aruntime image representative of an inspection area of the specimensharing the same design pattern as the given design pattern, andobtaining examination data specific for the given examinationapplication. The runtime image is acquired with a relatively low SNR(same/similar to the low SNR of the first training image as describedabove).

FIG. 5 shows a schematic illustration of a training process of a MLmodel in accordance with certain embodiments of the presently disclosedsubject matter. During the training phase, the training set as generatedin accordance with the description of FIGS. 2 and 4 can be obtained,comprising one or more training samples. Specifically, a first trainingimage 502 which is a low SNR image representative of a reference area ofa specimen is acquired. A second training image 504 which is a high SNRimage of the same reference area is acquired. The second training image504 is annotated to obtain label data (506) thereof (e.g., asegmentation map associated with the second training image, in anexample of a segmentation application). The two images 502 and 504 areregistered, such that either the label data or the first training image502 is fixed in accordance with the offset therebetween. A trainingsample, which includes the registered first training image 502 and labeldata 506, is thus generated. The training sample can be used to train(508) the ML model 510, thereby obtaining a trained ML modelcharacterized by segmentation-related training parameters. In someembodiments, the training process can be cyclic, and can be repeatedseveral times until the ML model is sufficiently trained, e.g., to beable to provide an output of a segmentation map meeting an accuracycriterion. By way of example, the ML model can be trained using a costfunction related to segmentation accuracy (e.g., the ground truth labeldata vs. predicted segmentation map).

Although only one training sample is illustrated in FIG. 5 , this is notintended to limit the present disclosure in any way. In someembodiments, one or more additional training samples can be obtained ina similar manner, and the training process can be repeated using theadditional training samples. In some cases, optionally, the trained MLmodel can be validated using a validation set of images. The validationset of images can be a different image set from the training set, andcan comprise images selected for validation purposes. A user can providefeedback for the results reached by the ML model during training orvalidation.

Referring now to FIG. 3 , there is illustrated a generalized flowchartof runtime examination of a semiconductor specimen using a trained ML inaccordance with certain embodiments of the presently disclosed subjectmatter.

A runtime image (e.g., a FP image as described above) representative ofan inspection area of the semiconductor specimen can be obtained (302)(e.g., by the examination tool 120) during runtime examination of thespecimen. The runtime image has a relatively low signal-to-noise ratio(SNR) (e.g., with respect to a high SNR as described herein, or withrespect to a threshold). By way of example, the runtime image can beacquired by an electron beam tool, such as, e.g., SEM.

The runtime image can be processed (304) using a machine learning (ML)model (e.g., the ML model 108 in PMC 102) to obtain examination dataspecific for a given examination application. The ML model can bepreviously trained for the given examination application using one ormore training samples, each representative of a respective referencearea sharing same design pattern as the inspection area, as describedabove with respect to FIGS. 2 and 4 . Specifically, each training samplecomprises a first training image (204) of the respective reference areahaving a relatively low signal-to-noise ratio (SNR) (e.g., relativelylower with respect to the high SNR or a threshold), and label data (206)indicative of ground truth in the respective reference area pertainingto the given examination application. The label data is obtained byannotating a second training image of the respective reference areahaving a relatively high SNR. As described above, the first trainingimage is acquired in a similar imaging condition as the runtime image(in terms of electron dose, number of frames, etc.), such that the lowSNR of the first training image is the same/similar (e.g., beingidentical, or similar to, or highly correlated with) as the low SNR ofthe runtime image. Thus, the low SNR of the first training image and thelow SNR of the runtime image can be similar/equivalent to a certainextent, and do not have to be exactly the same. Various similaritymeasures and algorithms can be used for determining the extent/level ofequivalence/similarity therebetween.

As aforementioned, the examination application referred to herein can beany application from a group comprising (but not limited to): asegmentation application for segmenting a runtime image into one or moresegments in an inspection area, a metrology application for obtainingone or more measurements for a structural feature in the inspectionarea, a defect detection application for detecting one or more defectcandidates in the inspection area, and a defect classificationapplication for classifying one or more defect candidates in theinspection area.

According to certain embodiments, the examination application can be asegmentation application. The term “segmentation” used herein may referto any process of partitioning an image into meaningful parts/segments(for example, background and foreground, noisy and non-noisy areas,various structural elements, defects and non-defects, etc.) whilstproviding per-pixel or per-region values indicative of such segments. Insuch cases, a training sample can include a first training image (e.g.,a SEM image) and label data which can be a ground truth segmentation mapcorresponding to the SEM image (e.g., indicative of a specific segmentof one or more segments that each pixel of at least part of the runtimeimage belongs to). By way of example, in some cases, the segments cancorrespond to one or more structural elements presented in the firsttraining image. A structural element used herein can refer to anyoriginal object on the image data that has a geometrical shape orgeometrical structure with a contour, in some cases combined with otherobject(s). A structural element can be presented, e.g., in the form of apolygon.

Upon the ML model being trained, the trained ML model can be used toprocess a runtime image and output a predicted segmentation map which isinformative of predicted labels associated with corresponding pixels inthe runtime image. Each predicted label is indicative of a segment inthe runtime image to which a respective pixel belongs.

FIG. 6 illustrates an example of a first training image in accordancewith certain embodiments of the presently disclosed subject matter. Thetraining image 602 is exemplified as a SEM image captured by a SEM tooland representing an area of a die of a wafer. As shown, there are aplurality of structural elements 604 (illustrated as polygonsrepresenting the elements of contacts on the wafer) presented in theimage. In the present example, the label data (obtained from annotationon a second training image) can be provided as a segmentation map suchas, e.g., a binary map representing two segments, the first segmentcorresponding to the structural elements in the image, and the secondsegment corresponding to the background area.

FIG. 7 is a schematic illustration of a runtime examination processusing a ML model in accordance with certain embodiments of the presentlydisclosed subject matter. During runtime, a runtime image 702 of aspecimen with a low SNR is acquired and processed by the trained MLmodel 704 to obtain examination data specific for a given examinationapplication. By way of example, in a segmentation application, theoutput examination data can be a segmentation map 706 corresponding tothe runtime image 702. The obtained segmentation map can be informativeof per-pixel or per-region segmentation labels indicative of differentsegments on the image. By way of example, the polygons on one layer canhave one segmentation label, and the polygons on another layer can havea different segmentation label, while the background can have a separatesegmentation label.

In some embodiments, such a segmentation map can be used by metrologytools for performing measurements on the specimen. By way of anotherexample, it can also be usable for ADC when constructing attributes(e.g., for defining if a defect is on the main pattern, on thebackground, or both), or for ADR for applying segment-specific detectionthresholds on each segment, etc.

In some embodiments, the examination application is a defectclassification application. The label data acquired in such cases can beinformative of classes of defects presented in a reference area (e.g.,particles, pattern deformation, bridges, etc.), and optionally, theprobabilities thereof for the defects to belong to these classes. A costfunction used during training of the ML can be based on classificationerrors between the predicted classes and the ground truth (class labels)thereof.

In some embodiments, the examination application is a defect detectionapplication. The label data acquired in such cases can be informative ofwhether a defect candidate from a list of defect candidates presented ina reference area is a defect of interest (DOI), or nuisance. Forinstance, the label data can be provided, e.g., in the form of boundingboxes of the DOIs, or in the form of a binary image in which only pixelsbelonging to DOIs get a value of “1”, and non-defective pixels get avalue of “0” etc. A cost function used during training of the ML can bebased on detection accuracy/capture rate, and, optionally, also based ona penalty for misdetection and over-detections.

In some embodiments, the examination application is a metrologyapplication. The label data acquired in such cases can be informative ofone or more measurements (e.g., CD measurements) with respect to astructural element in an inspection area. A cost function used duringtraining of the ML can be based on measurement accuracy of the predictedmeasurements with respect to the ground truth measurements.

According to some embodiments, the ML model can be implemented as aclassifier. The term “classifier”, “classifier model” or “classificationmodel” referred to herein should be broadly construed to cover anylearning model capable of identifying to which of a set ofcategories/classes a new instance belongs, on the basis of a trainingset. By way of example, in the exemplified segmentation application, theclassifier can be trained to classify the pixel candidates into a set ofsegment classes as defined by the user. The trained classifier can beused for image segmentation, i.e., for providing a predicted label foreach pixel in an image indicating the segment to which it belongs. It isto be noted that the classifier can be implemented as various types ofmachine learning models, such as, e.g., Linear classifiers, Supportvector machines (SVM), neural networks, decision trees, etc., and thepresent disclosure is not limited by the specific model implementedtherewith.

According to certain embodiments, the training process as describedabove with reference to FIGS. 2 and 4 can be included as part of aprocess for generating an examination recipe usable by system 101 and/orthe examination tool 120 for online examination in runtime (where the MLmodel, once trained, can serve as part of the examination recipe).Therefore, the presently disclosed subject matter also includes a systemand method for generating an examination recipe during a recipe setupphase as described with reference to FIGS. 2 and 4 (and variousembodiments thereof). It is to be noted that the term “examinationrecipe” should be expansively construed to cover any recipe that can beused by an examination tool for any examination application includingthe embodiments as described above.

It is to be noted that examples illustrated in the present disclosure,such as, e.g., the various ways of obtaining the first and secondtraining images, the exemplified examination applications and label datathereof, etc., are illustrated for exemplary purposes, and should not beregarded as limiting the present disclosure in any way. Otherappropriate examples/implementations can be used in addition to, or inlieu of the above.

Among advantages of certain embodiments of the training process asdescribed herein is that it enables to acquire annotation from a highSNR image and associate it with a low SNR image, where the label dataand the low SNR image together are used as a training sample fortraining a ML model. This ensures the accuracy of the acquired labeldata which serves as ground truth for the training, while enables the MLmodel to be trained on low SNR images having similar imaging conditionsas the runtime images that will be examined in runtime, thus improvingthe performance of the trained ML model in runtime examination in termsof robustness and accuracy.

Among further advantages of certain embodiments of the training processas described herein, is that it enables examination of certain sensitivelayers of a specimen which were previously not possible to be inspected,since these layers require minimal electron dose during inspection inorder to reduce pattern damage, which results in images with very lowSNR, and annotation is not feasible to be performed on such low SNRimages.

Among further advantages of certain embodiments of the training processas described herein is that, as annotation on an image of high SNR iseasier and faster, this further improves the user experience ofannotation and time to recipe (TTR) for training the ML.

It is to be understood that the present disclosure is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings.

It will also be understood that the system according to the presentdisclosure may be, at least partly, implemented on a suitably programmedcomputer. Likewise, the present disclosure contemplates a computerprogram being readable by a computer for executing the method of thepresent disclosure. The present disclosure further contemplates anon-transitory computer-readable memory tangibly embodying a program ofinstructions executable by the computer for executing the method of thepresent disclosure.

The present disclosure is capable of other embodiments and of beingpracticed and carried out in various ways. Hence, it is to be understoodthat the phraseology and terminology employed herein are for the purposeof description and should not be regarded as limiting. As such, thoseskilled in the art will appreciate that the conception upon which thisdisclosure is based may readily be utilized as a basis for designingother structures, methods, and systems for carrying out the severalpurposes of the presently disclosed subject matter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of thepresent disclosure as hereinbefore described without departing from itsscope, defined in and by the appended claims.

1. A computerized system of runtime examination of a semiconductorspecimen, the system comprising a processing and memory circuitry (PMC)configured to: obtain a runtime image representative of an inspectionarea of the semiconductor specimen, the runtime image having arelatively low signal-to-noise ratio (SNR); and process the runtimeimage using a machine learning (ML) model to obtain examination dataspecific for a given examination application, wherein the ML model ispreviously trained for the given examination application using one ormore training samples, each training sample representative of arespective reference area sharing same design pattern as the inspectionarea and comprising: a first training image of the respective referencearea having a relatively low SNR similar to the low SNR of the runtimeimage; and label data indicative of ground truth in the respectivereference area pertaining to the given examination application, thelabel data obtained by annotating a second training image of therespective reference area having a relatively high SNR.
 2. Thecomputerized system according to claim 1, wherein the runtime image andthe one or more training samples are acquired by an electron beam tool.3. The computerized system according to claim 1, wherein the givenexamination application is one of: a segmentation application forsegmenting the runtime image into one or more segments in the inspectionarea, a metrology application for obtaining one or more measurementswith respect to a structural element in the inspection area, a defectdetection application for detecting one or more defect candidates in theinspection area, and a defect classification application for classifyingone or more defects in the inspection area.
 4. The computerized systemaccording to claim 1, wherein the first training image is generatedbased on a first number of training frames acquired for the referencearea, and the second training image is generated based on a secondnumber of training frames acquired for the reference area, wherein thefirst number is smaller than the second number, and wherein the runtimeimage is generated based on the first number of runtime frames.
 5. Thecomputerized system according to claim 4, wherein the second number oftraining frames comprise the first number of training frames.
 6. Thecomputerized system according to claim 1, wherein the first trainingimage is generated based on a first dose of electrons, and the secondtraining image is generated based on a second dose of electrons, whereinthe first dose is less than the second dose, and wherein the runtimeimage is generated based on the first dose of electrons.
 7. Thecomputerized system according to claim 1, wherein the label data isobtained based on at least one of: manual annotation, syntheticallygenerated labels based on design data, machine learning derived labels,or a combination thereof.
 8. The computerized system according to claim1, wherein the first training image and the second training image areregistered to correct an offset therebetween, so that the first trainingimage and the label data are aligned.
 9. The computerized systemaccording to claim 1, wherein the first training image is acquired priorto acquisition of the second training image.
 10. The computerized systemaccording to claim 1, wherein the inspection area is a part of asensitive layer of the semiconductor specimen which is inspectable onlyvia images having a relatively low SNR.
 11. The computerized systemaccording to claim 1, wherein the given examination application is asegmentation application for segmenting the runtime image into one ormore segments in the inspection area, and the label data is indicativeof a specific segment of the one or more segments that each pixel of atleast part of the runtime image belongs to.
 12. The computerized systemaccording to claim 1, wherein the inspection area is from an inspectiondie of the semiconductor specimen, and the respective reference area isfrom a reference die of the inspection die from the semiconductorspecimen or from a different semiconductor specimen.
 13. A computerizedmethod of training a machine learning model usable for examining asemiconductor specimen, the method performed by a processing and memorycircuitry (PMC) and comprising: obtaining a training set comprising oneor more training samples, each training sample representative of arespective reference area having a given design pattern, the trainingsample comprising: a first training image having a relatively lowsignal-to-noise ratio (SNR); and label data indicative of ground truthin the respective reference area pertaining to the given examinationapplication, the label data obtained by annotating a second trainingimage of the respective reference area having a relatively high SNR; andtraining the ML model for the given examination application using thetraining set; wherein the ML model, upon being trained, is usable forprocessing a runtime image representative of an inspection area of thespecimen sharing same design pattern as the given design pattern andobtaining examination data specific for the given examinationapplication, the runtime image having a relatively low SNR similar tothe low SNR of the first training image.
 14. The computerized methodaccording to claim 13, wherein the given examination application is oneof: a segmentation application for segmenting the runtime image into oneor more segments in the inspection area, a metrology application forobtaining one or more measurements with respect to a structural elementin the inspection area, a defect detection application for detecting oneor more defect candidates in the inspection area, and a defectclassification application for classifying one or more defects in theinspection area.
 15. The computerized method according to claim 13,wherein the obtaining the training set comprises generating the firsttraining image based on a first number of training frames acquired forthe reference area, and generating the second training image based on asecond number of training frames acquired for the reference area,wherein the first number is smaller than the second number, and whereinthe runtime image is generated based on the first number of runtimeframes.
 16. The computerized method according to claim 13, wherein theobtaining the training set comprises generating the first training imagebased on a first dose of electrons, generating the second training imagebased on a second dose of electrons, wherein the first dose is less thanthe second dose, and wherein the runtime image is generated based on thefirst dose of electrons.
 17. The computerized method according to claim13, wherein the obtaining the training set comprises registering thefirst training image and the second training image to correct an offsettherebetween, so that the first training image and the label data arealigned.
 18. The computerized method according to claim 13, wherein thefirst training image is acquired prior to acquisition of the secondtraining image.
 19. The computerized method according to claim 13,wherein the given examination application is a segmentation applicationfor segmenting the runtime image into one or more segments in theinspection area, and the label data is indicative of a specific segmentof the one or more segments that each pixel of at least part of theruntime image belongs to.
 20. A non-transitory computer readable storagemedium tangibly embodying a program of instructions that, when executedby a computer, cause the computer to perform a method of runtimeexamination of a semiconductor specimen, the method comprising:obtaining a runtime image representative of an inspection area of thesemiconductor specimen, the runtime image having a relatively lowsignal-to-noise ratio (SNR); and processing the runtime image using amachine learning (ML) model to obtain examination data specific for agiven examination application, wherein the ML model is previouslytrained for the given examination application using one or more trainingsamples, each training sample representative of a respective referencearea sharing same design pattern as the inspection area and comprising:a first training image of the respective reference area having arelatively low SNR similar to the low SNR of the runtime image; andlabel data indicative of ground truth in the respective reference areapertaining to the given examination application, the label data obtainedby annotating a second training image of the respective reference areahaving a relatively high SNR.